blind source separation technique separates mixed signals blindly without any information on the mixing system. in this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. in these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. in order to evalu...

In this paper, the transportation problem under the carbon emission trading program ismodelled by mathematical programming and genetic algorithm. Since green supply chain issuesbecome important and new legislations are taken into account, carbon emissions costs are included inthe total costs of the supply chain. The optimisation model has the ability to minimise the total costsand provides the ...

The Californian Channel Islands are near-shore islands with high levels of endemism, but extensive habitat loss has contributed to the decline or extinction of several endemic taxa. A key parameter for understanding patterns of endemism and demography in island populations is the magnitude of inter-island dispersal. This paper estimates the extent of migration and genetic differentiation in thr...

In practice, obtaining the global optimum for the economic dispatch {bf (ED)}problem with ramp rate limits and prohibited operating zones is presents difficulties. This paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using aFuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm  deals  with the issue ofcontrolling the ex...

this paper considers the job scheduling problem in virtual manufacturing cells (vmcs) with the goal of minimizing two objectives namely, makespan and total travelling distance. to solve this problem two algorithms are proposed: traditional non-dominated sorting genetic algorithm (nsga-ii) and knowledge-based non-dominated sorting genetic algorithm (kbnsga-ii). the difference between these algor...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

this paper investigates the problem of selecting and scheduling a set of projects among available projects. each project consists of several tasks and to perform each one some resource is required. the objective is to maximize total benefit. the paper constructs a mathematical formulation in form of mixed integer linear programming model. three effective metaheuristics in form of the imperialis...

nowadays, by successful application of on time production concept in other concepts like production management and storage, the need to complete the processing of jobs in their delivery time is considered a key issue in industrial environments. unrelated parallel machines scheduling is a general mood of classic problems of parallel machines. in some of the applications of unrelated parallel mac...

this study aimed to estimate microsatellite based genetic diversity in two lines (the selected rirs and control line rirc) of rhode island red (rir) chicken. genomic dna of 24 randomly selected birds maintained at central avian research institute (india) and 24 microsatellite markers were used. microsatellite alleles were determined on 6% urea-page, recorded using geldoc system and the samples ...